Part I - Ford GoBike Exploration

by Mohamed OLOU

Introduction

This dataset includes information about individual trips made in a bike-sharing system covering the greater San Francisco Bay Area.

Introduce the dataset

Rubric Tip: Your code should not generate any errors, and should use functions, loops where possible to reduce repetitive code. Prefer to use functions to reuse code statements.

Rubric Tip: Document your approach and findings in markdown cells. Use comments and docstrings in code cells to document the code functionality.

Rubric Tip: Markup cells should have headers and text that organize your thoughts, findings, and what you plan on investigating next.

Preliminary Wrangling

# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

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Preliminary Wrangling

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Gathering

df_bike = pd.read_csv('201902-fordgobike-tripdata.csv')
df_bike.sample(3)

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
122633	228	2019-02-11 11:13:48.8130	2019-02-11 11:17:37.5710	239.0	Bancroft Way at Telegraph Ave	37.868813	-122.258764	247.0	Fulton St at Bancroft Way	37.867789	-122.265896	4483	Subscriber	1999.0	Female	No
151299	1248	2019-02-06 17:09:52.2320	2019-02-06 17:30:40.7890	151.0	53rd St at Hollis St	37.836182	-122.287180	244.0	Shattuck Ave at Hearst Ave	37.873676	-122.268487	5180	Subscriber	1986.0	Male	No
123047	937	2019-02-11 09:46:52.1570	2019-02-11 10:02:29.8430	284.0	Yerba Buena Center for the Arts (Howard St at ...	37.784872	-122.400876	223.0	16th St Mission BART Station 2	37.764765	-122.420091	4422	Customer	1990.0	Female	No

---

Accessing

df_bike.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   start_station_latitude   183412 non-null  float64
 6   start_station_longitude  183412 non-null  float64
 7   end_station_id           183215 non-null  float64
 8   end_station_name         183215 non-null  object 
 9   end_station_latitude     183412 non-null  float64
 10  end_station_longitude    183412 non-null  float64
 11  bike_id                  183412 non-null  int64  
 12  user_type                183412 non-null  object 
 13  member_birth_year        175147 non-null  float64
 14  member_gender            175147 non-null  object 
 15  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.4+ MB

There are some columns that don't have the right format (start_time, end_time, start_station_id, end_station_id, member_birth_year)

df_bike[["start_time", "end_time", "start_station_id", "end_station_id", "member_birth_year"]].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 5 columns):
 #   Column             Non-Null Count   Dtype  
---  ------             --------------   -----  
 0   start_time         183412 non-null  object 
 1   end_time           183412 non-null  object 
 2   start_station_id   183215 non-null  float64
 3   end_station_id     183215 non-null  float64
 4   member_birth_year  175147 non-null  float64
dtypes: float64(3), object(2)
memory usage: 7.0+ MB

There are many null value

df_bike.isnull().sum()

duration_sec                  0
start_time                    0
end_time                      0
start_station_id            197
start_station_name          197
start_station_latitude        0
start_station_longitude       0
end_station_id              197
end_station_name            197
end_station_latitude          0
end_station_longitude         0
bike_id                       0
user_type                     0
member_birth_year          8265
member_gender              8265
bike_share_for_all_trip       0
dtype: int64

There are some very old persons that are cyclist

df_bike.member_birth_year.unique()

array([1984.,   nan, 1972., 1989., 1974., 1959., 1983., 1988., 1992.,
       1996., 1993., 1990., 1981., 1975., 1978., 1991., 1997., 1986.,
       2000., 1982., 1995., 1980., 1973., 1985., 1971., 1979., 1967.,
       1998., 1994., 1977., 1999., 1987., 1969., 1963., 1976., 1964.,
       1965., 1961., 1968., 1966., 1962., 1954., 1958., 1960., 1970.,
       1956., 1957., 1945., 1900., 1952., 1948., 1951., 1941., 1950.,
       1949., 1953., 1955., 1946., 1947., 1931., 1943., 1942., 1920.,
       1933., 2001., 1878., 1901., 1944., 1928., 1934., 1939., 1930.,
       1902., 1910., 1938., 1927.])

There are some duration that are very long

df_bike.duration_sec.unique()

array([52185, 42521, 61854, ..., 13251,  5713,  2822], dtype=int64)

---

Cleaning

#Let make a copy of the dataset
new_df_bike = df_bike.copy()

Columns format

Define

There are some columns that don't have the right format (start_time, end_time, start_station_id, end_station_id, member_birth_year)

Cleaning

#Let create a function to convert a column format into datetime format
def formatToDateTime(dataframe,column):
    dataframe[column] = pd.to_datetime(dataframe[column])

#Let create a function to convert a column format into int format
def formatToInt(dataframe,column):
    dataframe[column] = dataframe[column].fillna(0).astype(int)

#Let convert start_time, end_time to datetime format
for column in ['start_time', 'end_time']:
    formatToDateTime(new_df_bike,column)
    
#Let convert start_station_id, end_station_id, member_birth_year to int format
for column in ['start_station_id', 'end_station_id', 'member_birth_year']:
    formatToInt(new_df_bike,column)
    

Test

new_df_bike[["start_time", "end_time", "start_station_id", "end_station_id", "member_birth_year"]].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 5 columns):
 #   Column             Non-Null Count   Dtype         
---  ------             --------------   -----         
 0   start_time         183412 non-null  datetime64[ns]
 1   end_time           183412 non-null  datetime64[ns]
 2   start_station_id   183412 non-null  int32         
 3   end_station_id     183412 non-null  int32         
 4   member_birth_year  183412 non-null  int32         
dtypes: datetime64[ns](2), int32(3)
memory usage: 4.9 MB

Null values

Define

There are some null values in the dataset

Cleaning

#Let check null values
new_df_bike.isnull().sum()

duration_sec                  0
start_time                    0
end_time                      0
start_station_id              0
start_station_name          197
start_station_latitude        0
start_station_longitude       0
end_station_id                0
end_station_name            197
end_station_latitude          0
end_station_longitude         0
bike_id                       0
user_type                     0
member_birth_year             0
member_gender              8265
bike_share_for_all_trip       0
dtype: int64

#Let know differents values of column member_gender
new_df_bike.member_gender.unique()

array(['Male', nan, 'Other', 'Female'], dtype=object)

#Let fill member_gender null value by Unknown
new_df_bike.member_gender.fillna("Unknown", inplace=True)

#Let notice that there was null values in start_station_id and end_station_id

#Those null values was fill by 0 while turning their format into int format

#Let create a function that returns the list of start_station_latitude in the dataframe with condtition
def listLatitude(condition):
    return [*set(new_df_bike[condition].start_station_latitude.to_list())]

#Let find list of start_station_latitude where start_station_id is 0
latitude_list_1 = listLatitude(new_df_bike.start_station_id==0)

#Let find list of start_station_latitude where end_station_id is 0
latitude_list_2 = listLatitude(new_df_bike.end_station_id==0)

#Let create fnction that compare two array
def compareArrays(array1, array2):
    if(np.array_equal(array1,array2)):
        print("The arrays are the same")
    else: print("The arrays are differents")

#Let know if the rows that don't have start_station_id are the same with the row that don't have end_station_id
compareArrays(latitude_list_1,latitude_list_2)

The arrays are the same

#Let find list of start_station_latitude where start_station_name is null
latitude_list_3 = listLatitude(new_df_bike.start_station_name.isna())

#Let find list of start_station_latitude where end_station_name is null
latitude_list_4 = listLatitude(new_df_bike.end_station_name.isna())

#Let know if the rows that don't have start_station_name are the same with the row that don't have end_station_name
compareArrays(latitude_list_3,latitude_list_4)

The arrays are the same

#Let know if the rows that don't have start_station_id are the same with the row that don't have end_station_id
compareArrays(latitude_list_1,latitude_list_4)

The arrays are the same

# Let check if the list latitude correspond to just these null values
new_df_bike[new_df_bike.start_station_latitude.isin(latitude_list_1)].info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 197 entries, 475 to 181201
Data columns (total 16 columns):
 #   Column                   Non-Null Count  Dtype         
---  ------                   --------------  -----         
 0   duration_sec             197 non-null    int64         
 1   start_time               197 non-null    datetime64[ns]
 2   end_time                 197 non-null    datetime64[ns]
 3   start_station_id         197 non-null    int32         
 4   start_station_name       0 non-null      object        
 5   start_station_latitude   197 non-null    float64       
 6   start_station_longitude  197 non-null    float64       
 7   end_station_id           197 non-null    int32         
 8   end_station_name         0 non-null      object        
 9   end_station_latitude     197 non-null    float64       
 10  end_station_longitude    197 non-null    float64       
 11  bike_id                  197 non-null    int64         
 12  user_type                197 non-null    object        
 13  member_birth_year        197 non-null    int32         
 14  member_gender            197 non-null    object        
 15  bike_share_for_all_trip  197 non-null    object        
dtypes: datetime64[ns](2), float64(4), int32(3), int64(2), object(5)
memory usage: 23.9+ KB

# We observed that the columns start_station_id, end_station_id, start_station_name and end_station_name have their null value at the same rows
# We also observed that the list of latitude just correspond to null values

#Let know if the id of the bike where we have null value are uniques
print(np.intersect1d(new_df_bike[~new_df_bike.start_station_latitude.isin(latitude_list_1)].bike_id.unique(), new_df_bike[new_df_bike.start_station_latitude.isin(latitude_list_1)].bike_id.unique()))

[]

# That mean that the null value is caused by some specific bike
new_df_bike[new_df_bike.start_station_latitude.isin(latitude_list_1)].bike_id.unique()

array([4211, 4174, 4283, 4089, 4150, 4091, 3975, 4168, 4239, 4250, 4124,
       4189, 4095, 4141, 4197, 4207, 4162, 4249, 4245, 4093, 4165, 4130,
       4092, 4125, 4275, 4157, 4251, 4208], dtype=int64)

# Let drop those specific rows to have a better exploration experience
new_df_bike.drop(index=new_df_bike[new_df_bike.start_station_latitude.isin(latitude_list_1)].index, inplace=True)

Test

new_df_bike.isnull().sum()

duration_sec               0
start_time                 0
end_time                   0
start_station_id           0
start_station_name         0
start_station_latitude     0
start_station_longitude    0
end_station_id             0
end_station_name           0
end_station_latitude       0
end_station_longitude      0
bike_id                    0
user_type                  0
member_birth_year          0
member_gender              0
bike_share_for_all_trip    0
dtype: int64

Old cyclist

Define

There are some very old cyclist

Cleaning

#Let get the age in new columns of the users for each trip
new_df_bike['Age'] = pd.to_datetime('now').year-new_df_bike.member_birth_year
new_df_bike['Age'].unique()

C:\Users\Issa\AppData\Local\Temp\ipykernel_4364\3528795979.py:2: FutureWarning: The parsing of 'now' in pd.to_datetime without `utc=True` is deprecated. In a future version, this will match Timestamp('now') and Timestamp.now()
  new_df_bike['Age'] = pd.to_datetime('now').year-new_df_bike.member_birth_year

array([  38, 2022,   50,   33,   48,   63,   39,   34,   30,   26,   29,
         32,   41,   47,   44,   31,   25,   36,   22,   40,   27,   42,
         49,   37,   51,   43,   55,   24,   28,   45,   23,   35,   53,
         59,   46,   58,   57,   61,   54,   56,   60,   68,   64,   62,
         52,   66,   65,   77,  122,   70,   74,   71,   81,   72,   73,
         69,   67,   76,   75,   91,   79,   80,  102,   89,   21,  144,
        121,   78,   94,   88,   83,   92,  120,  112,   84,   95])

#The oldest cyclist know age is 105 years old 
# Let assume that in our dataset the age more than 100 are wrong data

#Let get the cyclist than are more than 100 years old
len(new_df_bike[new_df_bike['Age']>100])

8338

# There 8,338 row that have a age more than 100 in our dataset of 183215 rows
# Let drop those rows
new_df_bike.drop(index=new_df_bike['Age'][new_df_bike['Age']>100].index, inplace=True)

Test

len(new_df_bike[new_df_bike['Age']>100])

0

Long duration trip

Define

There are some very long duration trip

Cleaning

#On average, a cyclist trip is 1 hour

#Let get the trip with more than 1 hour (3600s)
len(new_df_bike[new_df_bike.duration_sec>3600])

1386

# There 1,386 row that have dartion more than 3600s
# Let drop those rows
new_df_bike.drop(index=new_df_bike[new_df_bike.duration_sec>3600].index, inplace=True)

Test

len(new_df_bike[new_df_bike.duration_sec>3600])

0

Load in your dataset and describe its properties through the questions below. Try and motivate your exploration goals through this section.

What is the structure of your dataset?

There are 183,215 trips in the dataset with 16 features (duration_sec, start_time, end_time, start_station_id, start_station_name, start_station_latitude, start_station_longitude, end_station_id, end_station_name, end_station_latitude, end_station_longitude, bike_id, user_type, member_birth_year, member_gender, bike_share_for_all_trip).

Most variables are numeric in nature. But the varibales start_station_name, end_station_name, user_type, member_gender and bike_share_for_all_trip are type string and also start_time and end_time are datetime type.

The variables start_station_name, end_station_name are strings and specify the name of the starting station and the arrival station.
The variables user_type specify if the person who take the bike is a customer or suscriber.
The variables member_gender specify if the person who take the bike is gender male, female or other
The variables bike_share_for_all_trip specify if the bike is shared during the trip

What is/are the main feature(s) of interest in your dataset?

I'm most interested in figuring out what features are best for predicting the trip arrival in the dataset.

What features in the dataset do you think will help support your investigation into your feature(s) of interest?

I think the start_station_name, the age and the day will help to predict what the distance and duration that the user will make

Univariate Exploration

In this section, investigate distributions of individual variables. If

you see unusual points or outliers, take a deeper look to clean things up and prepare yourself to look at relationships between variables.

Rubric Tip: The project (Parts I alone) should have at least 15 visualizations distributed over univariate, bivariate, and multivariate plots to explore many relationships in the data set. Use reasoning to justify the flow of the exploration.

Rubric Tip: Use the "Question-Visualization-Observations" framework throughout the exploration. This framework involves asking a question from the data, creating a visualization to find answers, and then recording observations after each visualisation.

Rubric Tip: Visualizations should depict the data appropriately so that the plots are easily interpretable. You should choose an appropriate plot type, data encodings, and formatting as needed. The formatting may include setting/adding the title, labels, legend, and comments. Also, do not overplot or incorrectly plot ordinal data.

How long do most people ride ?

# Let start by looking at the distribution of the trip duration
sns.distplot(new_df_bike.duration_sec, bins=60)
plt.title('Distribution of bike duration')
plt.show()

C:\Users\Issa\AppData\Local\Temp\ipykernel_4364\2624141781.py:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(new_df_bike.duration_sec, bins=60)

new_df_bike.duration_sec.describe()

count    173491.000000
mean        618.488014
std         443.765147
min          61.000000
25%         321.000000
50%         507.000000
75%         780.000000
max        3596.000000
Name: duration_sec, dtype: float64

The chart show a distribution at right of the duration trip with a peak between 300 and 500. That mean that most of person ride between 5 and 8 min

How old are most cyclists ?

# Let look at the distribution of the Age in the dataset (boxplot)
sns.boxplot(data=new_df_bike, y='Age')
plt.title('Distribution of the age of users')
plt.show()

# Let look at the distribution of the Age in the dataset (histogram)
sns.distplot(new_df_bike.Age, bins=50)
plt.title('Distribution of the age of users')
plt.show()

C:\Users\Issa\AppData\Local\Temp\ipykernel_4364\2106324836.py:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(new_df_bike.Age, bins=50)

new_df_bike.Age.describe()

count    173491.000000
mean         37.155386
std           9.967410
min          21.000000
25%          30.000000
50%          35.000000
75%          42.000000
max          95.000000
Name: Age, dtype: float64

The charts show that the most people that use those bike for a trip are between 26 and 36 year old

What is the proportion of gender in this dataset ?

new_df_bike.member_gender.unique()

array(['Male', 'Female', 'Other'], dtype=object)

# Let see the proportion of the users genders.
sns.countplot(x='member_gender', data=new_df_bike)
plt.title('Propotion of user gender')
plt.show()

The chart shows that most of people that use those bike are man and the women are less than a third of men. After that we observe that few people are classified in other category.

When bike are most used ?

# Let create new column that will contain the hour when the bike are used
# new_df_bike['start_time'].year
new_df_bike['hour_of_use'] = pd.DatetimeIndex(new_df_bike['start_time']).hour
new_df_bike

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip	Age	hour_of_use
4	1585	2019-02-28 23:54:18.549	2019-03-01 00:20:44.074	7	Frank H Ogawa Plaza	37.804562	-122.271738	222	10th Ave at E 15th St	37.792714	-122.248780	4898	Subscriber	1974	Male	Yes	48	23
5	1793	2019-02-28 23:49:58.632	2019-03-01 00:19:51.760	93	4th St at Mission Bay Blvd S	37.770407	-122.391198	323	Broadway at Kearny	37.798014	-122.405950	5200	Subscriber	1959	Male	No	63	23
6	1147	2019-02-28 23:55:35.104	2019-03-01 00:14:42.588	300	Palm St at Willow St	37.317298	-121.884995	312	San Jose Diridon Station	37.329732	-121.901782	3803	Subscriber	1983	Female	No	39	23
7	1615	2019-02-28 23:41:06.766	2019-03-01 00:08:02.756	10	Washington St at Kearny St	37.795393	-122.404770	127	Valencia St at 21st St	37.756708	-122.421025	6329	Subscriber	1989	Male	No	33	23
8	1570	2019-02-28 23:41:48.790	2019-03-01 00:07:59.715	10	Washington St at Kearny St	37.795393	-122.404770	127	Valencia St at 21st St	37.756708	-122.421025	6548	Subscriber	1988	Other	No	34	23
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
183407	480	2019-02-01 00:04:49.724	2019-02-01 00:12:50.034	27	Beale St at Harrison St	37.788059	-122.391865	324	Union Square (Powell St at Post St)	37.788300	-122.408531	4832	Subscriber	1996	Male	No	26	0
183408	313	2019-02-01 00:05:34.744	2019-02-01 00:10:48.502	21	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	66	3rd St at Townsend St	37.778742	-122.392741	4960	Subscriber	1984	Male	No	38	0
183409	141	2019-02-01 00:06:05.549	2019-02-01 00:08:27.220	278	The Alameda at Bush St	37.331932	-121.904888	277	Morrison Ave at Julian St	37.333658	-121.908586	3824	Subscriber	1990	Male	Yes	32	0
183410	139	2019-02-01 00:05:34.360	2019-02-01 00:07:54.287	220	San Pablo Ave at MLK Jr Way	37.811351	-122.273422	216	San Pablo Ave at 27th St	37.817827	-122.275698	5095	Subscriber	1988	Male	No	34	0
183411	271	2019-02-01 00:00:20.636	2019-02-01 00:04:52.058	24	Spear St at Folsom St	37.789677	-122.390428	37	2nd St at Folsom St	37.785000	-122.395936	1057	Subscriber	1989	Male	No	33	0

173491 rows × 18 columns

sns.distplot(new_df_bike.hour_of_use, bins=24)
plt.title('Distribution of the hour of bike use')
plt.show()

C:\Users\Issa\AppData\Local\Temp\ipykernel_4364\1438815868.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(new_df_bike.hour_of_use, bins=24)

The chart show a bimodal distribution of the hour of use with a peak between 09:00 and 10:00 and between 17:00 and 19:00. That mean that most of people ride early morning before 10:00 or at evening after 17:00.

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?

To show the distribution of hour of use, we had to create new column from the start_time column

Bivariate Exploration

In this section, investigate relationships between pairs of variables in your

data. Make sure the variables that you cover here have been introduced in some fashion in the previous section (univariate exploration).

What is the correlation between numerical values in the dataset ?

# Let see the correlation with a heatmap
sns.heatmap(new_df_bike.corr(), annot=True, cmap="vlag_r")
plt.title('Correlation between numerical value')
plt.show()

C:\Users\Issa\AppData\Local\Temp\ipykernel_4364\11445466.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(new_df_bike.corr(), annot=True, cmap="vlag_r")

sns.pairplot(data=new_df_bike)
plt.title('Distributions between numerical values')
plt.show()

That charts show that there is no correlation between numerical variables

What genre rides the longest on average ?

sns.barplot(data=new_df_bike, x="member_gender", y="duration_sec")
plt.title('Average of duration by gender')
plt.show()

sns.pointplot(data=new_df_bike, x="member_gender", y="duration_sec")
plt.title('Average of duration by gender')
plt.show()

On average female gender last longer at cycling

What is the ralation between distance and age

from math import sin, cos, sqrt, atan2, radians
R = 6373.0
def getdistance(row):
    distlat = radians(row.end_station_latitude-row.start_station_latitude)
    distlong = radians(row.end_station_longitude-row.start_station_longitude)
    a = sin(distlat/2)**2 + cos(row.start_station_latitude) * cos(row.end_station_latitude) * sin(distlong/2)**2
    c = 2 * atan2(sqrt(a), sqrt(1 - a))
    distance = R * c
    return(distance)

new_df_bike['distance(km)'] = new_df_bike.apply(lambda row: getdistance(row), axis=1)
new_df_bike

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip	Age	hour_of_use	distance(km)
4	1585	2019-02-28 23:54:18.549	2019-03-01 00:20:44.074	7	Frank H Ogawa Plaza	37.804562	-122.271738	222	10th Ave at E 15th St	37.792714	-122.248780	4898	Subscriber	1974	Male	Yes	48	23	2.862401
5	1793	2019-02-28 23:49:58.632	2019-03-01 00:19:51.760	93	4th St at Mission Bay Blvd S	37.770407	-122.391198	323	Broadway at Kearny	37.798014	-122.405950	5200	Subscriber	1959	Male	No	63	23	3.478691
6	1147	2019-02-28 23:55:35.104	2019-03-01 00:14:42.588	300	Palm St at Willow St	37.317298	-121.884995	312	San Jose Diridon Station	37.329732	-121.901782	3803	Subscriber	1983	Female	No	39	23	2.220363
7	1615	2019-02-28 23:41:06.766	2019-03-01 00:08:02.756	10	Washington St at Kearny St	37.795393	-122.404770	127	Valencia St at 21st St	37.756708	-122.421025	6329	Subscriber	1989	Male	No	33	23	4.665105
8	1570	2019-02-28 23:41:48.790	2019-03-01 00:07:59.715	10	Washington St at Kearny St	37.795393	-122.404770	127	Valencia St at 21st St	37.756708	-122.421025	6548	Subscriber	1988	Other	No	34	23	4.665105
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
183407	480	2019-02-01 00:04:49.724	2019-02-01 00:12:50.034	27	Beale St at Harrison St	37.788059	-122.391865	324	Union Square (Powell St at Post St)	37.788300	-122.408531	4832	Subscriber	1996	Male	No	26	0	1.846593
183408	313	2019-02-01 00:05:34.744	2019-02-01 00:10:48.502	21	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	66	3rd St at Townsend St	37.778742	-122.392741	4960	Subscriber	1984	Male	No	38	0	1.505151
183409	141	2019-02-01 00:06:05.549	2019-02-01 00:08:27.220	278	The Alameda at Bush St	37.331932	-121.904888	277	Morrison Ave at Julian St	37.333658	-121.908586	3824	Subscriber	1990	Male	Yes	32	0	0.429293
183410	139	2019-02-01 00:05:34.360	2019-02-01 00:07:54.287	220	San Pablo Ave at MLK Jr Way	37.811351	-122.273422	216	San Pablo Ave at 27th St	37.817827	-122.275698	5095	Subscriber	1988	Male	No	34	0	0.762902
183411	271	2019-02-01 00:00:20.636	2019-02-01 00:04:52.058	24	Spear St at Folsom St	37.789677	-122.390428	37	2nd St at Folsom St	37.785000	-122.395936	1057	Subscriber	1989	Male	No	33	0	0.801833

173491 rows × 19 columns

# sns.scatterplot(data=new_df_bike, x="Age", y="duration_sec")
new_df_bike.plot.hexbin(x="Age", y="distance(km)", gridsize=15)
plt.title('Distribution between Age and distance')
plt.show()

That mean most of poeple that ride are between 26 and 36 year old and ride on average 1.25 km

What is the ralation between distance and member gender

sns.stripplot(x="member_gender",y="distance(km)",data=new_df_bike)
plt.title('Distribution and distance by member_gender')
plt.show()

The plot show that the longest distance is made by a man, the second by other gender and the third by a woman

What is the ralation between distance and user type

sns.barplot(data=new_df_bike, x="user_type", y="distance(km)")
plt.title('Average of duration by user type')
plt.show()

This plot show that in average, customer make longest distance than suscriber

Multivariate Exploration

Create plots of three or more variables to investigate your data even

further. Make sure that your investigations are justified, and follow from your work in the previous sections.

What is the distribution of age in relation to the different values of gender and user type

grid = sns.FacetGrid(new_df_bike, col="member_gender", row="user_type")
grid.map(plt.hist, 'Age')
# plt.title('Distribution of age based on user type and member gender')
plt.show()

c:\Python310\lib\site-packages\seaborn\axisgrid.py:745: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  plot_args = [v for k, v in plot_data.iteritems()]
c:\Python310\lib\site-packages\seaborn\axisgrid.py:745: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  plot_args = [v for k, v in plot_data.iteritems()]
c:\Python310\lib\site-packages\seaborn\axisgrid.py:745: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  plot_args = [v for k, v in plot_data.iteritems()]
c:\Python310\lib\site-packages\seaborn\axisgrid.py:745: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  plot_args = [v for k, v in plot_data.iteritems()]
c:\Python310\lib\site-packages\seaborn\axisgrid.py:745: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  plot_args = [v for k, v in plot_data.iteritems()]
c:\Python310\lib\site-packages\seaborn\axisgrid.py:745: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  plot_args = [v for k, v in plot_data.iteritems()]

This plot shows that most of users are male suscriber and are on average 25 to 35 years old

What is the relation between age and distance in relation to the different values of gender and user type

sns.lmplot(data=new_df_bike, x="Age", y="distance(km)", col="member_gender", row="user_type")
# plt.title('Relation between distance and age based on user type and member gender')
plt.show()

This plot shows that there is no relation between distance and age

What is the relation between gender and distance in relation to the different values of gender and bike sharing

sns.catplot(data=new_df_bike, x="member_gender", y="distance(km)", col="bike_share_for_all_trip", row="user_type")
# plt.title('Relation between distance and age based on user type and member gender')
plt.show()

c:\Python310\lib\site-packages\seaborn\categorical.py:292: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  sub_data.loc[:, self.cat_axis] = adjusted_data
c:\Python310\lib\site-packages\seaborn\categorical.py:292: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  sub_data.loc[:, self.cat_axis] = adjusted_data
c:\Python310\lib\site-packages\seaborn\categorical.py:292: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  sub_data.loc[:, self.cat_axis] = adjusted_data

This plot shows that customers dont share their bike in trip

#Let store new dataset to csv file

new_df_bike.to_csv('FordGoBike.csv', index=False)